1. Field
The following description relates to an Internet Protocol television (IPTV) service technology, and more particularly, to a technology for recommending personalized content based on user preference.
2. Description of the Related Art
An Internet Protocol Television (IPTV) service, which is a broadcast-communication integrated service, enables content providers to provide multi-channel broadcasting services, Video on Demand (VoD) services, digital video recorders (DVR) services, etc., as well as existing services such as IP-based interactive multimedia services and digital broadcasting services, via an IP-based communication network. Following the development of IPTV services, the types and amount of content that is provided has become more and more various and large in size.
In general, when using an IPTV service, a user selects and uses desired content by searching for a title, actor, producer, keyword, etc. of the content through an electronic program guide (EPG) with a terminal, for example, with a set-top box which supports IPTV services. However, if too many types of content are found or too much content is found, it will be difficult for the user to find desired content. This means that accuracy of searching is poor and accordingly the user's satisfaction with the IPTV service is lowered. In order to resolve this problem, studies into technologies for recognizing what content is demanded by users to perceive the users' preference and recommending personalized content to the users based on the users' preference are being developed.
Most known content recommending methods are based on content recommendation according to content preference information received and stored in advance from users. However, the conventional methods recommend content based on relatively correct preference information as users are required to themselves input information regarding their preference, but users have the inconvenience of having to update information regarding their preference whenever changes occur in preference. Also, the conventional methods do not reflect the importance between content description parameters in representing personal preference. In other words, the conventional methods have failed to perceive a content description parameter most influencing each user's preference among content description parameters, such as a content title, actor, producer, keyword, etc.